A rule based approach to data certification - applying DQXML for system independent data certification

Many researchers and practitioners have been attracted to improve data quality due to its monumental importance as a key success factor. Mathematical and statistical models have been deployed to information systems to introduce constrain and transaction based mechanisms to prevent data quality related problems. Entire management of the process and roles involved in data generation has also been scrutinized. Vast amount of knowledge base progressed in this area are mostly limited from practical perspective. Quality related meta data is absent from most information systems. Neither process mapping nor data modelling provides sufficient provision to measure quality or certification of data in the information systems. Furthermore, on-going monitoring of data for quality conformance through a separate process is expensive and time consuming. Recognising this limitation and aiming to provide a practical-orient comprehensive approach, I propose a process centric quality focused solution incorporating data product quality, conformance monitoring and certification. I base my work on DQXML developed by Ismael Caballero and deploy rigour of design science to construct InfoGuard. InfoGuard consists of DQXML incorporating quality meta data and an independent data quality monitor that provides certification of data through a rule based process centric framework for on-going data quality monitoring

Fault Diagnosis in Enterprise Software Systems Using Discrete Monitoring Data

Success for many businesses depends on their information software systems. Keeping these systems operational is critical, as failure in these systems is costly. Such systems are in many cases sophisticated, distributed and dynamically composed. To ensure high availability and correct operation, it is essential that failures be detected promptly, their causes diagnosed and remedial actions taken. Although automated recovery approaches exists for specific problem domains, the problem-resolution process is in many cases manual and painstaking. Computer support personnel put a great deal of effort into resolving the reported failures. The growing size and complexity of these systems creates the need to automate this process. The primary focus of our research is on automated fault diagnosis and recovery using discrete monitoring data such as log files and notifications. Our goal is to quickly pinpoint the root-cause of a failure. Our contributions are: Modelling discrete monitoring data for automated analysis, automatically leveraging common symptoms of failures from historic monitoring data using such models to pinpoint faults, and providing a model for decision-making under uncertainty such that appropriate recovery actions are chosen. Failures in such systems are caused by software defects, human error, hardware failures, environmental conditions and malicious behaviour. Our primary focus in this thesis is on software defects and misconfiguration